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Proceedings Paper

Granulometric classifiers from small samples
Author(s): Yoganand Balagurunathan; Ronaldo F. Hashimoto; Seungchan Kim; Junior Barrera; Edward R. Dougherty
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Paper Abstract

Morphological granulometries and their moment features are used as shape descriptors. These features find application in classification, segmentation and estimation. Design of classifiers has been a primary goal of most pattern recognition problems. Small sample design is often a constraint when designing classifiers. We use a recently proposed small sample design method in which the sample observations are spread with a probability mass and the classifiers designed on the spread mass. The designed classifiers are more reliability for relative to the population. Two issues are addressed: design of granulometric classifiers using a small sample, and granulometric classification based on a very small number of features.

Paper Details

Date Published: 22 May 2002
PDF: 8 pages
Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); doi: 10.1117/12.467971
Show Author Affiliations
Yoganand Balagurunathan, Texas A&M Univ. (United States)
Ronaldo F. Hashimoto, Univ. de Sao Paulo (United States)
Seungchan Kim, National Human Genome Project/National Institutes of Health (United States)
Junior Barrera, Univ. de Sao Paulo (Brazil)
Edward R. Dougherty, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 4667:
Image Processing: Algorithms and Systems
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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